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Advanced SQL and data engineering for AI: CTEs, window functions, design, transactions, indexes, security, Python and pandas, data cleaning, ETL pipelines, ML-ready datasets, pgvector and warehouses — every example tested.
Twenty-two pages for after the beginner tutorial. The first half makes you strong in SQL itself: CTEs and recursive queries, window functions, real analytics patterns (growth, cohorts, retention), database design and normalization, transactions and isolation, indexes and query plans, views and triggers, and security.
The second half is data handling for AI: Python and SQLAlchemy, cleaning messy data with pandas, file formats, reliable ETL pipelines, building leakage-free ML datasets, SQL inside AI apps (logs, JSON, full-text search and vector search with pgvector) and how warehouses and big-data tools fit. It ends with interview practice and capstone projects. Every query and program was run and its output checked, on SQLite, MySQL and PostgreSQL. English and Bangla.
6 chapters · 384 min total
Watch it taught, then check what stuck.